H∞ control for fractional order neural networks with uncertainties subject to deception attacks via Improved memory-event-triggered scheme and Its application - Banu, K. Asmiya; Aparna, T.; Tajudeen, M. Mubeen; Rajchakit, Grienggrai; Huang, Tingwen (Neural networks, 2025)
The article discusses an improved memory-event-triggered strategy for H∞ control class of fractional-order neural networks (FONNs) with uncertainties, which are vulnerable to deception attacks. The system under consideration is simultaneously influenced by external disturbances, network-induced time delays, uncertainties, and deception attacks. The suggested enhanced memory event-triggered framework enhances communications security measures and conserves network bandwidth compared to standard control strategies. Recently released packets are saved at the event generator and controller sides to develop the triggered events and construct the memory-based controller. A new type of fractional order neural network model is developed in consideration of both the impacts of enhanced memory-event-triggered strategy and deception attacks. By constructing suitable Lyapunov-Krasovskii functionals (LKFs), the asymptotic stability criterion with a H∞ performance index is derived. Moreover, the controller gain and weighting matrices can be obtained by solving the linear matrix inequalities (LMIs). Finally, illustrative examples, including a Chua’s diode circuit system, are used to prove the correctness of the suggested control technique.